RBtest (version 1.1)

RBtest.iter: Test of missing data mechanism using all available information

Description

This function tests MCAR vs MAR by using both the complete and incomplete variables.

Usage

RBtest.iter(data,K)

Arguments

data

Dataset with at least one complete variable.

K

Maximum number of iterations.

Value

A list of the following elements:

• abs.nbrMD The absolute quantity of missing data per variable.

• rel.nbrMD The percentage of missing data per variable.

• K The maximum admitted number of iterations.

• iter The final number of iterations.

• type.final Vector of the same length than the number of variables of the dataset, where '0' is for variables with MCAR data, '1' is for variables with MAR data and '-1' is for complete variables.

• TYPE.k Dataframe containing the type of missing data after each iteration. Each row is a vector having the same length than the number of variables of the dataset, where '0' is for variables with MCAR data, '1' is for variables with MAR data and '-1' is for complete variables.

Examples

# NOT RUN {
set.seed(60)
n<-100 # sample size
r<-5 # number of variables
mis<-0.2 # frequency of missing data
mydata<-matrix(NA, nrow=n, ncol=r) # mydata is a matrix of r variables
# following a U(0,1) distribution
for (i in c(1:r)){
	mydata[,i]<-runif(n,0,1)
}
bin.var<-sample(LETTERS[1:2],n,replace=TRUE, prob=c(0.3,0.7)) # binary variable [A,B].
# The probability of being in one of the categories is 0.3.
cat.var<-sample(LETTERS[1:3],n,replace=TRUE, prob=c(0.5,0.3,0.2)) # categorical variable [A,B,C].
# The vector of probabilities of occurence A, B and C is (0.5,0.3,0.7).
num.var<-runif(n,0,1) # Additional continuous variable following a U(0,1) distribution
mydata<-cbind.data.frame(mydata,bin.var,cat.var,num.var,stringsAsFactors = TRUE)
# dataframe with r+3 variables
colnames(mydata)=c("v1","v2","X1","X2","X3","X4","X5", "X6") # names of columns
# MCAR on X1 and X4 by using v1 and v2. MAR on X3 and X5 by using X2 and X6.
mydata$X1[which(mydata$v1<=sort(mydata$v1)[mis*n])]<-NA # X1: (mis*n)% of MCAR data.
# All data above the (100-mis)th percentile in v1 are selected
# and the corresponding observations in X1 are replaced with missing data.
mydata$X3[which(mydata$X2<=sort(mydata$X2)[mis*n])]<-NA # X3: (mis*n)% of MAR data.
# All data above the (100-mis)th percentile in X2 are selected
# and the corresponding observations in X3 are replaced with missing data.
mydata$X4[which(mydata$v2<=sort(mydata$v2)[mis*n])]<-NA # X4: (mis*n)% of MCAR data.
# All data above the (100-mis)th percentile in v2 are selected
# and the corresponding observations in X4 are replaced with missing data.
mydata$X5[which(mydata$X6<=sort(mydata$X6)[mis*n])]<-NA # X5: (mis*n)% of MAR data.
# All data above the (100-mis)th percentile in X6 are selected
# and the corresponding observations in X5 are replaced with missing data.
mydata$v1=NULL
mydata$v2=NULL

RBtest.iter(mydata,5)

# }